In production conditions, the recommender system would rely on provenance
records of pipeline executions launched by arbitrary users. While this would increase
the amount of data available, potentially resulting in more accurate
recommendations, it would also come with challenges. For instance, while
our framework models the execution outcome of a given pipeline-dataset pair
as a binary variable (success or failure), different execution outcomes may
be produced for a given pipeline-dataset pair, due to different
parametrizations or analysis types. Besides, analyses launched by less
experimented users may produce misleading provenance records. We anticipate
that the recommender could be configured to use implicit feedback to address
this issue [1]}.
